Mrkdwn Analysis

https://github.com/yannbanas/mrkdwn_analysis
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Mrkdwn Analysis is a powerful Python library designed to analyze Markdown files, providing extensive...
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License:MIT
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### Install mrkdwn_analysis

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Install the library using pip.

```bash
pip install markdown-analysis
```

--------------------------------

### Global Analysis Summary

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Perform a comprehensive analysis of the Markdown document to get a summary of its elements.

```python
analysis = analyzer.analyse()
print(analysis)
# {
#   'headers': X,
#   'paragraphs': Y,
#   'blockquotes': Z,
#   'code_blocks': A,
#   'ordered_lists': B,
#   'unordered_lists': C,
#   'tables': D,
#   'html_blocks': E,
#   'html_inline_count': F,
#   'words': G,
#   'characters': H
# }
```

--------------------------------

### Retrieve Document Statistics

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Get advanced statistics about the document, including reading time, complexity metrics, link statistics, and word frequency.

```python
# Get reading time
reading_time = doc.get_reading_time()
print(reading_time['formatted'])  # "5 min read"

# Document complexity metrics
complexity = doc.get_complexity_metrics()
print(f"Complexity score: {complexity['complexity_score']}")

# Link statistics
link_stats = doc.get_link_statistics()
print(f"External links: {link_stats['external_links']}")

# Word frequency analysis
top_words = doc.get_word_frequency(top_n=20)
```

--------------------------------

### Initialize MarkdownAnalyzer

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Import the MarkdownAnalyzer class and create an instance with the path to your Markdown file.

```python
from mrkdwn_analysis import MarkdownAnalyzer

analyzer = MarkdownAnalyzer("path/to/document.md")
```

--------------------------------

### Analyze Markdown File

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Analyze a specific Markdown file ('example.md') and print the identified elements.

```python
analyzer = MarkdownAnalyzer("example.md")

print(analyzer.identify_headers())
# {"Header": [{"line": X, "level": 1, "text": "Python 3.11"}, {"line": Y, "level": 3, "text": "Performance Details"}]}

print(analyzer.identify_paragraphs())
# {"Paragraph": ["A major **Python** release ...", "This paragraph contains inline HTML: ..."]}

print(analyzer.identify_html_blocks())
# [{"line": Z, "content": "<div class=\"note\">
  <p>HTML block example</p>
</div>"}]

print(analyzer.identify_html_inline())
# [{"line": W, "html": "<span style=\"color:red;\">Red text</span>"}]

print(analyzer.identify_lists())
# {
#   "Ordered list": [["Ordered list item 1", "Ordered list item 2"]],
#   "Unordered list": [["A basic point", "A task to do [Task]", "A completed task [Task done]"]]
# }

print(analyzer.identify_code_blocks())
# {"Code block": [{"start_line": X, "content": "import math\nprint(math.factorial(10))", "language": "python"}]}

print(analyzer.analyse())
# {
#   'headers': 2,
#   'paragraphs': 2,
#   'blockquotes': 1,
#   'code_blocks': 1,
#   'ordered_lists': 2,
#   'unordered_lists': 3,
#   'tables': 0,
#   'html_blocks': 1,
#   'html_inline_count': 1,
#   'words': 42,
#   'characters': 250
# }
```

--------------------------------

### Identify Headers

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Extract all headers from the Markdown document.

```python
headers = analyzer.identify_headers()
```

--------------------------------

### Export Document to Various Formats

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Export document content to JSON, HTML with optional styling, or plain text with formatting stripped.

```python
# Export to JSON
json_output = doc.to_json(include_metadata=True)

# Export to HTML with styling
html_output = doc.to_html(include_style=True)

# Export to plain text
plain_text = doc.to_plain_text(strip_formatting=True)
```

--------------------------------

### Identify Links

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Extract all links from the Markdown document.

```python
links = analyzer.identify_links()
```

--------------------------------

### Identify Paragraphs

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Extract all paragraphs from the Markdown document.

```python
paragraphs = analyzer.identify_paragraphs()
```

--------------------------------

### Identify Code Blocks

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Extract code blocks, including their content and language, from the Markdown document.

```python
code_blocks = analyzer.identify_code_blocks()
```

--------------------------------

### Identify Lists

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Extract both ordered and unordered lists, including tasks, from the Markdown document.

```python
lists = analyzer.identify_lists()
```

--------------------------------

### Check Broken Links

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Validate text links in the Markdown document to identify any broken links.

```python
broken_links = analyzer.check_links()
```

--------------------------------

### Identify Inline HTML

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Extract inline HTML elements from the Markdown document.

```python
inline_html = analyzer.identify_html_inline()
```

--------------------------------

### Identify HTML Blocks

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Extract standalone HTML blocks from the Markdown document.

```python
html_blocks = analyzer.identify_html_blocks()
```

--------------------------------

### Check for Broken Links in Document

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Efficiently check all links within the document for broken URLs using parallel processing. Reports broken links with their status codes or error messages.

```python
# Parallel link checking (much faster!)
broken_links = doc.check_links(max_workers=10)
for link in broken_links:
    print(f"Broken: {link['url']} - {link.get('status_code', 'error')}")
```

--------------------------------

### Search and Filter Document Content

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Use the search function to find specific text within the document. Filter headers by their level or generate a table of contents up to a specified depth.

```python
# Search for content
results = doc.search("Python", case_sensitive=False)

# Find headers by level
h2_headers = doc.find_headers_by_level(2)

# Generate table of contents
toc = doc.get_table_of_contents(max_level=3)
```

--------------------------------

### Extract Code by Language

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Extract all code blocks from the document that are identified as belonging to a specific programming language, such as Python.

```python
# Extract Python code blocks
python_code = doc.extract_code_by_language('python')
for block in python_code:
    print(block['content'])
```

--------------------------------

### Validate Document Structure

Source: https://github.com/yannbanas/mrkdwn_analysis/blob/main/README.md

Validate the overall structure of the document, receiving a validity status, a score, and a list of identified issues with their types and messages.

```python
# Validate document structure
validation = doc.validate_structure()
print(f"Valid: {validation['valid']}, Score: {validation['score']}/100")
for issue in validation['issues']:
    print(f"[{issue['type']}] {issue['message']}")
```
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